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Signal Analyzer

Signal Analyzer evaluates indicator values against a target asset's future outcome such as return, win rate, or volatility. Use it to test a signal before turning it into a strategy rule.

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Video walkthrough

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When to use it

  • Use Signal Analyzer to test whether an indicator relates to a later outcome across time periods.
  • Use Portfolio Backtest when you already know the rule and want to test it inside a portfolio.
  • Use Strategy Builder when the next step is a signal-driven switching rule.

Modes

ModePrimary inputsPrimary results
SingleOne indicator, one target ticker, explicit price mode, one forward metric, and one horizon.Scatter and fit view, bin summaries, split coverage, and diagnostics.
DualTwo indicators measured jointly against shared forward outcomes.Metric heatmap, populated-cell diagnostics, bin table, and cell-level save-to-library selection.
MultiFeature set, tree controls, and train/test validation settings.Tree summary, feature importance, leaf-level reporting metrics, and validation diagnostics.

Core inputs

  • Indicator: the predictor you test, such as price, SMA, EMA, RSI, trailing return, volatility, or drawdown.
  • Signal asset: the asset used to compute each indicator.
  • Transform: an optional subtract or divide operation against a second indicator. In single mode, it is under Compare with another indicator. The second indicator starts as a different indicator on the same ticker, because an indicator against itself is a constant.
  • Target ticker: the asset whose future behavior the forward metric measures.
  • Signal return basis: whether indicators and target outcomes use total-return series or price-only raw closes. Total return is the default. Uploaded U.* aliases and unsupported .SIM raw series use total_return with a warning.
  • Forward metric: the future outcome, such as forward return, win rate, or volatility.
  • Horizon: the number of trading days between the signal observation and the measured outcome.

Research controls

Each mode shows its question first: the indicators, the target, and the forward outcome. The controls below are tokens on one line under the inputs, and each token shows its current values. Select a token to edit it. Single mode uses Sampling and fit. Dual mode uses Sampling, metrics, and bins. Multi mode uses Sampling and reporting and Tree model.

  • Sampling frequency: how often the analysis samples the daily history.
  • Signal lag: shifts the sampling schedule so you can check the same analysis against nearby alignments.
  • Splits: divides the sample into time slices for stability checks.
  • Fit method and bins: single-mode controls for polynomial fit or regressogram summaries.
  • Metric selection: dual mode lets you switch the displayed heatmap metric while keeping the same sample.
  • Tree controls: multi-mode settings such as train/test split, depth, minimum leaf size, and validation folds.

Read the results

  • Single mode: start with scatter and fit, then check diagnostics for horizon, sampling assumptions, correlation, and split coverage before reading bin means.
  • Dual mode: each heatmap axis shows the indicator range of each bin. Use cell coverage and the best and worst cells to separate broad effects from sparse outliers. Then save a cell to the Library.
  • Multi mode: compare train/test and cross-validation diagnostics, then inspect leaf-level means and rule paths before creating a signal from a leaf.

Regressogram summaries use full-precision bin membership. Display rounding does not change which observations a bin contains.

Save to Library

Save an analysis to keep its inputs and diagnostics. To reuse a pattern in Strategy Builder, save the single setup, dual cell, or multi leaf as a Library signal.